AI in Finance Summit New York schedule

•    How AI/ML is leveraged to enhance customer interactions in financial services through personalized recommendations, AI-driven chatbots, and predictive analytics, creating more engaging and efficient customer experiences?
•    Exploring how AI/ML technologies are improving the financial product cycle and enabling the creation of new financial products and services
•    What key factors must be balanced to ensure the successful and responsible implementation of AI?

 

 

Motivated by persistent pressures in counter-FinCrime, the session will address synthetic dataset generation typologies and their efficacy in fraud and money laundering use cases, as an area of growing interest. Boosted by new Generative AI capabilities, the option for tailoring datasets to desired use cases for model training, opens new avenues for enhanced coverage of possible fraud landscapes. Simulated datasets promise to overcome the ‘Collective Intelligence conundrum’, that is, the inability to share transaction data for knowledge discovery across networks, on account of PII protection and privacy constraints. The questions now become more acute as to (i) how useful synthetic data can be to train models, as measured for utility, privacy and fidelity, and (ii) what are the limits between generative capability and data representativeness - and how far we can and should go with simulated data.

•    Ensuring that your Training Data is accurate, bias free and trustworthy 
•    Successful practices to tackle bias and improve model outcomes 
•    Promoting accountability for tracking and communicating the lineage of AI-driven outcomes and stress testing models 
•    Applying individually-centric design principles to products and services  

 

•    What is the next chapter in the usage of Data in AI?    
•    What is the next chapter in AI with respect to the framework of models, computational infrastructure, and social implications?
•    How do these new trends in Data and AI improve customer experience, risk management, and operational efficiency in the financial services industry?

Panelists:
John Chan, Director of Technology - AI/ML, Raymond James
Karamjit Singh, Director, Artificial Intelligence, Mastercard
Maurice Leon, Director, Data Assets, Data Management, CIBC Mellon
Harry Mendell, Data Architect Technology Group, Federal Reserve Bank of New York

Moderator: Snehit Cherian, VP, CTO, Global Nexis Solutions

•    How AI and ML are revolutionizing traditional financial products and services, such as lending, insurance, and wealth management? 
•    Discovering the potential of AI and ML to create new revenue streams and improve profitability for financial institutions 
•    The future directions of AI and ML in financial products, emerging technologies and potential applications 

 

•    What are the key challenges financial institutions face when transitioning from AI hype to practical implementation, and how can they overcome them?
•    How can banks ensure that AI solutions are aligned with both regulatory requirements and business goals to deliver real value?
•    What role does data quality and governance play in the successful adoption of AI in financial services, and what steps should organizations take to improve it?
•    How can financial institutions manage the risks associated with AI, such as biases in algorithms and cybersecurity threats, while still maximizing the technology’s potential?

Panelists:
Reema Gill, Senior Regulatory Consultant, AI Governance and Oversight, CIBC
Thomas Pellet, Data Management Lab, Bloomberg 
Maddie Daianu, Head of Data & AI, Intuit Credit Karma
Marsal Gavalda, Chief Technology Officer, Clarity AI

Moderator: Georgios Samakovitis, Professor of FinTech, Greenwich University

Legacy systems and tech debt can slow innovation, making modernization feel like an uphill battle. This session cuts through the noise to show how AI can streamline the process, reducing months of manual effort into days. We’ll tackle a common question: "What does my code even do?" and explore how AI accelerates version upgrades, testing, and refactoring. Finally, we’ll showcase a live demo of AI mapping a spaghetti codebase—bringing clarity to complexity. Whether you're facing modernization challenges or just curious about AI's role, this talk offers practical insights for a smarter, faster approach to software evolution.

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•    How can we ensure that AI models used in banking are fair, unbiased, and ethically sound?
•    How can we make AI models more transparent and explainable to both internal stakeholders and customers?
•    What are the best practices for communicating the decision-making process of AI systems?
•    How can banks navigate the evolving regulatory landscape for AI and machine learning?

•    Building a strong foundation for AI: The importance of ensuring data is accurate, complete, and consistent to train reliable AI Models
•    Defining the critical role of data governance in protecting sensitive customer information
•    Outlining the core principles that should guide your organization's data governance efforts
•    Identifying the role of data governance tools and platforms in automating processes and improving data quality

In this keynote, we’ll explore how AI is transforming fraud prevention in the financial and fintech industries. We'll discuss the evolution of fraud threats, traditional methods, and how AI is enhancing fraud detection. While AI is revolutionizing the field, it also introduces challenges like bias and ethical concerns. By the end of the session, you'll gain insights into how AI is reshaping fraud prevention, its opportunities, and the steps businesses must take to stay ahead of emerging threats.

•    Building a financial services-specific AI strategy
•    Integrating AI with existing business processes and systems
•    Establishing key performance indicators (KPIs) for AI adoption

Panelists:
Tyler Frieling, Director Emerging Technologies, Blackrock
Haonan Wang, Principal Machine Learning Engineer, GoFi
Ipsita Basu, Product Lead, Shopify

•    Overcoming data security, fraud detection, and scalability in AI-powered payment solutions
•    How AI is driving innovation in real-time payments, personalized customer experiences, and cost optimization
•    Exploring the role of AI in shaping the next generation of payment systems and industry standards

•    How are AI and ML revolutionizing traditional financial products and services, such as lending, insurance, and wealth management?
•    Discovering the potential of AI and ML to create new revenue streams and improve profitability for financial institutions
•    The future directions of AI and ML in financial products, emerging technologies and potential applications

Akhil Khunger, VP Quantitative Analytics, Barclays
Terrence Muthoka, Senior Data Scientist, Citi
Kristi Kunworee Baishya, AI Product Lead, Nomura Holding America
Frederic Repond, Vice President, Strategy & Innovation, Prudential

Kristi Kunworee Baishya, AI Product Lead, Nomur

•    Prepare and manage trusted data to unlock the full potential of AI in business operations.
•    Focus on the most valuable AI opportunities that deliver measurable results.
•    Develop skilled teams and robust infrastructures to scale AI across the organization.
•    Embed fairness, explainability, and ethics into AI systems to maintain trust and accountability
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•    Does artificial intelligence increase productivity as expected?
•    What are the biggest challenges financial institutions face when implementing AI solutions?
•    How can human expertise be best combined with AI to create a more robust financial ecosystem?
•    Implementing a Data-Driven approach

•    Evolution of Generative AI in the Workplace - From Personalization to Impersonation: Exploring the shift from AI enhancing personalized experiences to AI performing tasks that mimic human behavior, with cross-industry examples showcasing AI's diverse applications.
•    Human-AI Collaboration - Enhancing Capabilities and Ethical Considerations: How AI complements human work to increase productivity and efficiency, while addressing ethical concerns about AI impersonation and maintaining human interaction.
•    Future of Work- Adapting to and Predicting Change: Strategies for adapting to the evolving AI landscape and potential future scenarios, from utopian collaboration to dystopian job loss.

•    How are tech stack decisions changing with AI in finance?
•    Where are companies seeing the biggest AI infrastructure challenges?
•    What are the challenges in operationalizing data for AI?
•    Key steps towards addressing and improving return on investment

Panelists:
Richa Singh, Director and Head of Data and AI, Lexington Partners 
Aishwarya Ashok, Senior Data Scientist, PURE Insurance
Dyuti Dave, Technology Analyst, Barclays PLC
Tomasz Noetzel, Senior Equity Analyst, Bloomberg Intelligence

Use cases in Anti Money Laundering, Financial Crimes Compliance
User case in managing Regulatory Development

With the rapid rise of Generative AI (GAI), artificial intelligence has taken center stage for both businesses and regulators alike. As regulations proliferate, staying informed about AI legislative and policy developments across various jurisdictions has become critical. 
Ideally, regulating AI should unlock its potential in a clear, risk-informed manner. But do these regulations achieve that goal? Or do they risk stifling innovation and hindering growth instead?

•    What are the key challenges banks face in keeping up with the rapidly evolving landscape of AI regulations?
•    How can Large Language Models (LLMs) be leveraged to streamline the process of comparing AI regulations?
•    What are the specific benefits of using LLMs for regulatory compliance, particularly in terms of efficiency, accuracy, and insights gained?


 

•    Identifying how LLMs are transforming the analysis of unstructured financial data
•    How LLMs are enabling the development of innovative fintech product
•    Reviewing the current NLP trends and latest SOTA algorithms Overview of the latest NLP algorithms and industry use cases that are easier to solve using the open-source NLP methods
 

•    How can we ensure that the development and application of generative AI in quantitative finance aligns with ethical principles and avoids biases?
•    What are the specific data requirements for training and fine-tuning generative AI models in the context of quantitative finance? How can we address data quality and quantity challenges?
•    Given the complexity of generative AI models, how can we enhance their interpretability and explainability to build trust and facilitate regulatory compliance?

•    How open banking is reshaping data access and analytics by providing a more comprehensive view of financial data?
•    How can AI improve the customer experience within open banking? 
•    What can we expect to see from FinTech in the near future and what impacts will this have? 

Speakers:
Varun Pandey, Vice President, Payments Technology, JPMorgan Chase & Co.
Varun Awasthi, Vice President, JPMorgan Chase & Co.
Santosh Kumar Nayak, Lead Software Engineer, JPMorgan Chase & Co.